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1,221 results for “Aggregators”
Figure 10 in Discrete aggregate analysis of ovoid egg shapes in various bird species
Figure 10. Schemes of taking measurements to calculate wurfs for eggs of different types: a - true ovoids; b, c - symmetric and asymmetric pseudoovoids.
Figure 1 in Discrete aggregate analysis of ovoid egg shapes in various bird species
Figure 1. Geometric constructor figures for constructing ovoids: (a) Vesica piscis with circles inside, (b) A fragment of the matrix to obtain ovoids.
Fig. 1 in Larval pheromone disrupts pre-excavation aggregation of Cactoblastis cactorum (Lepidoptera: Pyralidae) neonates precipitating colony collapse
Fig. 1. Percent survival of caterpillars in cohorts of Cactoblastis cactorum on plants sprayed with caterpillar extract (gray bar), solvent-only (white bar), or unsprayed (black bar) for 4 separate experiments. Experiment 1 = laboratory study; experiment 2 = greenhouse study; experiment 3 = field study 1; experiment 4 = field study 2.
D-PLACE aggregated dataset
<p>Cite the source of the dataset as:</p> <blockquote> <p>Kathryn R. Kirby, Russell D. Gray, Simon J. Greenhill, Fiona M. Jordan, Stephanie Gomes-Ng, Hans-Jörg Bibiko, Damián E. Blasi, Carlos A. Botero, Claire Bowern, Carol R. Ember, Dan Leehr, Bobbi S. Low, Joe McCarter, William Divale, and Michael C. Gavin. (2016). D-PLACE: A Global Database of Cultural, Linguistic and Environmental Diversity. PLoS ONE, 11(7): e0158391. doi:10.1371/journal.pone.0158391.</p> </blockquote>
An exploration of human γD-crystallin affinity for potential aggregation inhibitors: A molecular docking investigation
<p><span>These files contain the supplementary material to the article “An exploration of human γD-crystallin affinity for potential aggregation inhibitors: A molecular docking investigation” and the molecular docking simulation data.</span></p> <p><span>In this study, we performed a comparative molecular docking analysis of several experimentally investigated molecules of natural origin, that might protect γ-crystallins from destabilization and aggregation. Our specific protein targets are wild-type human γD-crystallin, and its mutant P23T γD-crystallin, associated with congenital cataract. Thirteen phytochemicals were investigated as potential inhibitors of γD-crystallin aggregation, and we compared their binding energies with those of lanosterol, an ingredient present in over-the-counter eye products, to prevent cataracts. We performed a detailed comparative molecular docking analysis and we found that the binding energies of lanosterol outcompete those of all the other investigated potential natural inhibitors.</span></p>
Transgenic A53T mice have astrocytic a-synuclein aggregates in dopamine and striatal regions
<p>Quantification of astrocyte expression, co-expression of astrocytes with a-syn, and astrocyte morphological data (soma size and number of processes) from 6 month transgenic A53T PD mice.</p>
A3DyDB: exploring structural aggregation propensities in the yeast proteome.
<h3>The data from the paper: Garcia-Pardo, J., Badaczewska-Dawid, A.E., Pintado-Grima, C. <em>et al.</em> A3DyDB: exploring structural aggregation propensities in the yeast proteome. <em>Microb Cell Fact</em> <strong>22</strong>, 186 (2023). https://doi.org/10.1186/s12934-023-02182-3</h3> <h4>The unified and integrated metadata accompanied by referencing identifiers in the A3D database is available for download in CSV format.</h4> <h4>The unified and integrated metadata accompanied by referencing identifiers in the A3D database is available for download in CSV format.</h4>
A3D Model Organism Database (A3D-MODB): a database for proteome aggregation predictions in model organisms
<p>The unified and integrated metadata accompanied by referencing identifiers from the A3D database is available for download in CSV format.</p> <p>Aleksandra E Badaczewska-Dawid, Aleksander Kuriata, Carlos Pintado-Grima, Javier Garcia-Pardo, Michał Burdukiewicz, Valentín Iglesias, Sebastian Kmiecik, Salvador Ventura, A3D Model Organism Database (A3D-MODB): a database for proteome aggregation predictions in model organisms, <em>Nucleic Acids Research</em>, Volume 52, Issue D1, 5 January 2024, Pages D360–D367, <a href="https://doi.org/10.1093/nar/gkad942">https://doi.org/10.1093/nar/gkad942</a></p>
Linked collectors and determiners for: Resolution of the Aleiodes seriatus (Herrich-Schäffer, 1838) - aggregate in the western Palaearctic (Hymenoptera, Braconidae, Rogadinae), with description of a new species.
Natural history specimen data linked to collectors and determiners held within, "Resolution of the Aleiodes seriatus (Herrich-Schäffer, 1838) - aggregate in the western Palaearctic (Hymenoptera, Braconidae, Rogadinae), with description of a new species". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/d747aacc-514b-42fa-b8ef-d6384ce082c5">https://bionomia.net/dataset/d747aacc-514b-42fa-b8ef-d6384ce082c5</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/d747aacc-514b-42fa-b8ef-d6384ce082c5">https://gbif.org/dataset/d747aacc-514b-42fa-b8ef-d6384ce082c5</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Aggregated occurrence records of invasive European frog-bit (Hydrocharis morsus-ranae L.) across North America.
Natural history specimen data linked to collectors and determiners held within, "Aggregated occurrence records of invasive European frog-bit (Hydrocharis morsus-ranae L.) across North America". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/71454d8a-6e9c-49f5-bf37-353f9ad2e2b9">https://bionomia.net/dataset/71454d8a-6e9c-49f5-bf37-353f9ad2e2b9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/71454d8a-6e9c-49f5-bf37-353f9ad2e2b9">https://gbif.org/dataset/71454d8a-6e9c-49f5-bf37-353f9ad2e2b9</a>. Formatted as a Frictionless Data package.
uncropped western blots for analysis of RPN13 ubiquitylation and NRF1 activation by protein aggregates, as well as source data for qPCR plots and flow cytometry gating and FCS files for agDD-GFP in HeLa or HEK cells
<p>This entry contains uncropped blots for Fig 4D and Fig S4C, Fig. 5B, Fig S5 and Fig S6, and the raw FCS files for Flow Cytometry data in doi.org/10.1101/2024.08.30.610524.</p>
Vertically pointing doppler radar profiles (24 GHz Metek MRR-2) at Concordia Station (Dome C, Antarctica), aggregated to 5min, monthly netCDF archive
<p>Vertical profiles along the first three kilometres of atmosphere above the ground (from 300 to 3000 m AGL) of equivalent radar reflectivity factor (Ze), Doppler velocity (W) and Doppler spectral width (Sw) from a 24-GHz vertically pointing Micro Rain Radar MRR-2 by METEK GmbH positioned at Concordia Station (Dome C, Antarctica).</p> <p>Metadata available at <a href="https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/6dc25ff0-4c03-4ca8-af0d-cba06a411dc2" target="_blank" rel="noopener">https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/6dc25ff0-4c03-4ca8-af0d-cba06a411dc2</a> </p> <p>--------------------------------------------------------------------</p> <p>Example of netCDF file structure:</p> <h2><strong>File "DMC_MRR_MeK_201901_5min.nc"</strong></h2> <pre><strong> dimensions</strong>: <em>range </em>= 31; <em>time </em>= UNLIMITED; // (7736 currently) <strong>variables</strong>: float <em>Ze</em>(range=31, time=7736); :description = "Equivalent reflectivity factor relative to the most significant peak, dealiased, 5min non-logarithmic average. NaN means clear sky at the specified height."; :units = "dBZ"; :_ChunkSizes = 31U, 1U; // uint long <em>time_UTC(time=7736);</em> :description = "Measurement time. Timestamp indicates the end of the aggregation interval, e.g. 01-Mar-2020 00:05:00 represents the average of the variables between 01-Mar-2020 00:00:01 and 01-Mar-2020 00:05:00."; :time_zone = "UTC"; :units = "Seconds since 1970-01-01 00:00:00 (Unix time)."; :_ChunkSizes = 512U; // uint float <em>W</em>(range=31, time=7736); :description = "Mean Doppler Velocity of the most significant peak, dealiased, 5min average. Value not available in clear-sky conditions."; :units = "m s^-1"; :_ChunkSizes = 31U, 1U; // uint float <em>height</em>(range=31, time=7736); :description = "Height above instrument."; :units = "m"; :_ChunkSizes = 31U, 1U; // uint float <em>spectralWidth</em>(range=31, time=7736); :description = "Doppler Spectral Width of the most significant peak, dealiased, 5min average. Value not available in clear-sky conditions."; :units = "m s^-1"; :_ChunkSizes = 31U, 1U; // uint // <strong>global attributes</strong>: :<em>title </em>= "Micro rain radar data processed with IMProToo (Maahn, M. and Kollias, P., 2012), aggregated to 5min, monthly netCDF archive."; :<em>comment </em>= "IMProToo has been developed for improved snow measurements. Note that this data has been processed regardless of precipitation type."; :<em>time_label </em>= "Jan 2019"; :<em>source </em>= "Micro Rain Radar 2 (MRR-2), METEK GmbH, at DMC (Antarctica), frequency: 24 GHz, power: 50 mW, antenna diameter: 60 cm [https://metek.de/product/mrr-2/]"; :<em>institution </em>= "CNR-INO, Florence (IT)"; :<em>contact_person </em>= "Gianluca Di Natale, CNR-INO, Florence (IT), gianluca.dinatale@ino.cnr.it"; :<em>location </em>= "Concordia Station (Dome C, Antarctica, 75°06\'S, 123°21\'E, 3233 m a.s.l.)"; :<em>author </em>= "Giacomo Roversi, Ca\' Foscari University, Venice (IT) and CNR-ISAC, Rome (IT), g.roversi@isac.cnr.it"; :<em>creation_date </em>= "22-Oct-2024 17:32:24 UTC"; :<em>coverage </em>= "Monthly coverage (Jan 2019): 90.3226 %"; :<em>time_resolution </em>= "5 minutes"; :<em>history </em>= "Created with IMProToo v0.107 [https://github.com/maahn/IMProToo], aggregated to 5 minutes temporal resolution with an average of the 1-minute values if least 3 out of 5 are not NaN."; </pre>
Aggregate Dataset on Descriptive Representation in the British Parliament (2017-2024)
<p>This is the dataset aggregated at the legislature/party level by the CSIC team from the individual-level data provided by the Sciences Po and CSIC teams on descriptive representation in the British lower chamber of Parliament for WP4 of the ActEU project. </p>
Aggregate Dataset on Descriptive Representation in the Austrian Parliament (2017-2024)
<p>This is the dataset aggregated at the legislative/party level by the CSIC team from the individual-level data provided by the PLUS team on descriptive representation in the Austrian lower chamber of Parliament for WP4 of the ActEU project. </p>
Monthly precipitation intensity maxima for 14 aggregation times at 132 stations in Germany
<p>This dataset contains monthly precipitation intensity maxima for 14 aggregation times at 132 stations in Germany that serve as the basis of the study<em> Modeling seasonal variations of extreme rainfall on different time scales in Germany.</em></p> <p> </p> <p><strong>Generation of the dataset:</strong><br> We use precipitation measurements at 132 stations in Germany that provide a temporal resolution of one minute. The majority (129) of these stations are operated by the German Meteorological Service (DWD) and were obtained via ftp://ftp-cdc.dwd.de/climate_environment/CDC/observations_germany/climate. The available time series at these stations range from 19 to 28 years. Additionally we use three stations operated by the Wupperverband (https://www.wupperverband.de) with time series of more than 43 years.</p> <p>The observations were accumulated to the following durations: <span class="math-tex">\(d \in 2^{\left\lbrace 0,1,2,..,13 \right\rbrace}\,\text{min} = \left\lbrace 1,2,4,...,8192 \right\rbrace\,\text{min}\)</span>. Thus, resulting in 14 time series per station.</p> <p> </p> <p><strong>Files and variables</strong></p> <p>meta_data_seasonal_variations_IDF_germany.csv</p> <ul> <li>Meta information about the stations</li> <li>Variables: station name, station id (as provided by DWD), position (longitude, latitude), length of timeseries available</li> <li>Variable names: "StationName", "StationID", "Longitude", "Latitude", "NumberYears"</li> </ul> <p>monthly_maxima_seasonal_variations_IDF_germany.csv</p> <ul> <li>Monthly maxima for different durations (aggregation times)</li> <li>Variables: station id (as in meta file), year and month of observation, duration [h], observed monthly intensity maximum [mm/h]</li> <li>Variable names: "StationID", "Year", "Month", "Duration [h]", "MonthlyIntensityMaximum [mm/h]"</li> </ul> <p> </p> <p><strong>Abstract of the study</strong></p> <p>We model monthly precipitation maxima at 132 stations in Germany for a wide range of durations from one minute to about six days using a duration-dependent generalized extreme value (d-GEV) distribution with monthly varying parameters. This allows for the estimation of both monthly and annual intensity--duration--frequency (IDF) curves:<br> (1) The monthly IDF curves are steeper in summer and exhibit higher intensities for short durations than in the rest of the year. Thus, everywhere in Germany short convective extreme events occur very likely in summer. In contrast, extreme events with a duration of several hours up to about one day are more likely to occur within a longer period or even spread throughout the whole year, depending on the station. There are major differences within Germany with respect to the months in which long-lasting stratiform extreme events are more likely to occur. At some stations the IDF curves (for a given quantile) for different months intersect. The meteorological interpretation of this intersection is that the season at which a certain extreme event is most likely to occur shifts from summer towards autumn or winter for longer durations.<br> (2) We compare the annual IDF curves resulting from the monthly model with those estimated conventionally, that is, based on modeling annual maxima. We find that adding information in the form of smooth variations during the year leads to a considerable reduction of uncertainties. We additionally observe that at some stations, the annual IDF curves obtained by modeling monthly maxima deviate from the assumption of scale invariance, resulting in a flattening in the slope of the IDF curves for long durations.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The authors would like to thank the Wupperverband, and in particular Marc Scheibel, as well as the Climate Data Center of the DWD, for providing and maintaining the precipitation time series.</p>
Evaluation of the Economic Situation in the Arab World (Aggregated by County)
<p>The Arab Barometer Wave V 2018-2019 is based on a nationally representative probability sample of the population aged 18 and above. In most countries, the sample includes 2,400 citizens. The data were conducted in face-to-face public opinion surveys (CAPI and PAPI). See technical reports by country for country-specific information. You can find the data, codebooks and all relevant information on the Arab Barometer website.</p> <p>Our dataset contains country weighted counts of different answer options and the re-weighted values of the answers given to the Arab Barometer Wave 5 question:</p> <p>Q101 : How would you evaluate the current economic situation in your country? Very good, Good, Bad, Very bad our Don’t know, Refused to answer.</p> <p>See PDF documentation for details</p>
Figure 22 in Phylogenetic analysis of the Niphargus orcinus species- aggregate (Crustacea: Amphipoda: Niphargidae) with description of new taxa
Figure 22. Niphargus polymorphus sp. n., holotype. Pereopods III–IV, detail of pereopod IV dactylus. Retinacle of pleopod II. Uropods I–III. Telson.
Figure 18 in Phylogenetic analysis of the Niphargus orcinus species- aggregate (Crustacea: Amphipoda: Niphargidae) with description of new taxa
Figure 18. Niphargus lourensis sp. n., holotype. Pereopods III–IV, detail of pereopod IV dactylus. Uropods I–III. Telson.
Figure 14 in Phylogenetic analysis of the Niphargus orcinus species- aggregate (Crustacea: Amphipoda: Niphargidae) with description of new taxa
Figure 14. Niphargus dabarensis sp. n., holotype. Pereopods III–IV, detail of pereopod IV dactylus. Uropods I–III. Telson.
Figure 3 in Phylogenetic analysis of the Niphargus orcinus species- aggregate (Crustacea: Amphipoda: Niphargidae) with description of new taxa
Figure 3. Distribution of characters ''antenna I-length'' (character 19, CI50.18, RI50.53; left) and ''gnathopod II article 6 size'' (character 39, CI50.33, RI50.7; right). Long antennae are considered as troglomorphic, whereas a large-sized gnathopod II is supposed to be a synapomorphy of ''Orniphargus'' (S. Karaman (1950c)).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.